We evaluate the performance of a deep learning framework for segmenting abdominal fat and muscle using multi-contrast Dixon magnetic resonance (MR) and computed tomography (CT) images. We aim to compare MR image segmentation by testing Dixon fat-only, water-only, and combination of both types of images and comparing the results with CT images. Nineteen subjects underwent abdominal CT and Dixon MR imaging on the same day. For each participant, three pairs of matched axial images from both CT and MR were selected at the intervertebral levels of L2-L3, L3-L4, and L4-L5 for analysis. References labels were generated through semi-automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and muscle areas. They were then used to train and evaluate a U-Net based Convolutional Neural Network (CNN) framework with a 3-fold cross-validation to compare the segmentation performance across CT, Dixon fat-only and water-only MR images. Combining the fat-only and water-only MR image inputs produced superior results in all labels. Our study demonstrates that CNN-based segmentation performance for abdominal fat and muscle improves with the inclusion of additional input channels, such as combining Dixon fat-only and water-only MR images. While CT results represent the gold standard in abdominal image segmentation, increasing the number of input image channels used in MR segmentation can approach, and even match, the results of CT.

This study evaluates the agreement between Dixon-based MRI and CT in quantifying abdominal muscle and adipose tissue areas, aiming to establish MRI as an accurate, radiation-free alternative to the CT gold standard. Twenty subjects underwent abdominal CT and Dixon MRI on the same day, with matched axial images at L2-L3 and L4-L5 analyzed using semi-automatic software to contour boundaries, apply intensity thresholding, followed by manual refinement of the fat and muscle masks. Bland-Altman plots and linear regression analyses revealed strong agreement between MRI and CT for muscle and subcutaneous adipose tissue (SAT) areas, with mean differences of -0.02 cm2and - 1.13 cm2and limits of agreement within ±20.46 cm2and ±34.71 cm2, respectively, while visceral adipose tissue (VAT) showed larger discrepancies, likely due to compression of the abdomen during MRI, with a mean difference of -18.58 cm2and a limit of agreement of 20.34 cm2. Linear regression confirmed strong correlations with R2values of 0.89 for muscle, 0.98 for SAT, and 0.93 for VAT. These findings support MRI as a precise and radiation-free alternative for body composition analysis, particularly for muscle and SAT.

Cardiovascular disease, the leading cause of death in the U.S., affects both the heart and blood vessels. Contrast-enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for assessing heart and coronary artery morphology, aiding in the diagnosis of cardiovascular disease. Cine CTA, which captures multiple 3D heart images throughout the cardiac cycle, is particularly useful for evaluating nonischemic cardiomyopathy, a common cause of heart failure unrelated to coronary artery disease. Key biomarkers, such as left ventricular ejection fraction, which measures the amount of blood pumped from the heart’s lower chambers per contraction, and myocardial strain, which evaluates the deformation of the heart muscle during contraction and relaxation, are vital for assessing heart function. We developed an automated artificial intelligence (AI) framework to segment various cardiac structures across all image volumes representing different phases of the cardiac cycle. The framework also automatically extracts three short-axis slices to calculate the myocardial strain in the left ventricle. The results are compared with strain measurements obtained from cardiovascular magnetic resonance (CMR) cine imaging. Our study shows both radial and circumferential strains from cine CTA are comparable to those from cine CMR. The proposed AI framework facilitates a comprehensive evaluation of myocardial function throughout the cine CTA, supporting improved diagnostic accuracy.

Modern multidimensional medical imaging technology produces very large amount of data especially from the computed tomography modality. These volumetric dataset opens new demands for big data storage and high-speed communication systems which may be alleviated by image compression techniques. Current image compression schemes adopted in the DICOM standard do not exploit the inter-slice correlation within three-dimensional (3D) dataset. Video compression may have a potential to improve the compression ratio by reducing the redundancy in volumetric medical images. In this paper, we compare the performance of five lossless video codecs (H264, H265, Lagarith, MSU, MLC) and three still-image codecs (JPEG, JPEG2000, JPEG-LS) using 3D medical computed tomography datasets. Performance evaluation shows that video codecs improve the compression ratio compared to JPEG and JPEG2000 while being competitive to JPEG-LS.